全膝关节置换个体化患者右转步态的骨肌多体动力学仿真

目的构建个体化患者全膝关节置换(total knee replacement,TKR)的骨肌多体力学模型,模拟患者下肢右转步态时体内膝关节的生物力学行为。方法以1位具体患者的相关数据为材料,基于骨肌动力学仿真软件Any Body及其依赖于力的运动学建模方法,建立与患者相对应的TKR下肢骨肌多体动力学模型,并对患者的右转步态进行模拟。通过逆动力学分析右转步态,同时预测患者膝关节接触力、关节运动、肌肉活性和韧带力。结果模型预测的胫骨-股骨关节内、外侧接触力的均值均方根误差分别为285、164 N,相关系数分别为0.95和0.61,预测的髌骨接触力均值最大值为250 N。模型预测的接触力和肌肉活性与患者实验测量结果基本一致。此外,模型预测的胫骨-股骨的伸展弯曲、内外旋和内外翻运动的均值分布范围分别为3°~47°、-3.4°~1.5°、0.2°~-1.5°,胫骨-股骨的前后、上下和内外侧平移的运动范围分别为2.6~9.0 mm、1.6~3.2 mm、4.2~5.2 mm。模型还预测了内、外侧旁系韧带力和后交叉韧带力,其最大值分别为190、108、108 N。结论所开发的模型能够预测人工膝关节体内生物力学行为,为后续研究膝关节假体临床失效问题提供强有力的计算平台。

Musculoskeletal multi-body dynamic simulation on patient-specific total knee replacement during right-turn gait

Objective To develop a musculoskeletal multi-body dynamic model of the patient-specific total knee replacement (TKR), and to simulate knee joint biomechanical characters of the patient during right-turn gait. Methods Based on the musculoskeletal dynamic software AnyBody and the method of force-dependent kinematics as well as the related data from a patient with TKR, the corresponding patient specific lower extremity musculoskeletal multi-body dynamic model was constructed and then used to simulate the right-turn gait of the patient. The knee contact forces, motion, muscle activations and ligament forces were predicted simultaneously by inverse dynamics analysis on such right-turn gait. ResultsThe root mean square error of the predicted average tibiofemoral medial contact force and lateral contact force were 285 N and 164 N, respectively, and the correlation coefficients were 0.95 and 0.61, respectively. The predicted average patellar contact force was 250 N. The predicted contact forces and muscle activations were consistent with those in vivo measurements obtained from the patient. In addition, the model also predicted the average range of tibiofemoral rotations of flexion-extension, internal-external, varus-valgus as 3°-47°, -3.4°-1.5°, 0.2°--1.5°, and the average range of tibiofemoral translations of anterior-posterior, inferior-superior, medial-lateral as 2.6-9 mm, 1.6-3.2 mm, 4.2-5.2 mm, respectively. The predicted average peak value of the medial, lateral collateral ligament force and posterior cruciate ligament force were 190, 108, 108 N, respectively. Conclusions The developed model can predict in vivo knee joint biomechanics, which offers a robust computational platform for future study on the failure mechanisms of knee prosthesis in clinic.